Autonomous (Agentic) Data Pipelines for Self-Managing Lakehouses

Abstract

The introduction of agentic systems of autonomy into enterprise data engineering is a paradigm shift that enables the operational complexity of contemporary lakehouse structures. Big language models are highly problematic when deployed to production data contexts, especially the ability to comprehend domain-specific schemas and produce consistent transformations between distributed pipeline processes. Intelligent automation of essential processes such as schema drift correction, query optimization, pipeline debugging, data quality correction, and resource allocation is made possible by multi-agent solutions that consist of reasoning agents to analyze diagnostic information, execution agents to interact with infrastructure, and critic agents to validate these agents. Patterns of architecture underline message-carrying substrates of asynchronous coordination, plans of orchestration between choreographed autonomy and centralized control, and safety measures such as confidence thresholding, blast radius restricting, and an extensive audit trail. Production deployments show a significant increase in operational density with respect to shorter mean time to recover, massive cost savings through automated resource optimization, and better reliability through sustained performance tuning. Implementation strategies deal with tradeoffs in the choice of frameworks, patterns of integration of heterogeneous compute engines, and schemes of continuous learning that allow further improvement with the ongoing experience of operation of agents. The combination of agentic artificial intelligence and distributed data infrastructure makes it possible to have self-organizing systems that ensure the quality of services provided, and the manual intervention needs are cut by far, which places organizations in a competitive position because it delivers analytics more rapidly, and the platform can be scaled sustainably.